# Testing how to load rarfile from Zenodo, specifically https://zenodo.org/record/4661645. # https://github.com/huggingface/datasets/blob/dfdd2f949c1840926c02ae47f0f0c43083ef0b1f/datasets/common_voice/common_voice.py#L661 provided some inspiration # also https://huggingface.co/docs/datasets/master/dataset_script.html # coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TODO: Add a description here.""" import csv import json import os import datasets import pandas as pd import rarfile # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ _HOMEPAGE = "https://zenodo.org/record/466164" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "first_domain": "https://zenodo.org/record/4661645/files/Keyword_spotting_dataset_v0.01_17042021.rar", } class TempAfricaNLPKeywordSpottingForAfricanLanguages(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig( name="first_domain", version=VERSION, description="This part of my dataset covers a first domain", ), ] DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if ( self.config.name == "first_domain" ): # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "sentence": datasets.Value("string"), "path": datasets.Value("string"), "audio": datasets.features.Audio() # TODO: sampling rate? https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Audio # TODO: 'id', 'client_id', 'path', 'sentence', 'original_sentence_id', 'created_at', 'bucket', 'locale_id' } ) else: # This is an example to show how to have different features for "first_domain" and "second_domain" features = datasets.Features( { "sentence": datasets.Value("string"), "option2": datasets.Value("string"), "second_domain_answer": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) data_dir = os.path.join( data_dir, "data_17042021" ) # the rar file has a subfolder. return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "clips.xlsx"), "split": "train", "data_dir": data_dir, }, ), # # datasets.SplitGenerator( # name=datasets.Split.TEST, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": os.path.join(data_dir, "test.jsonl"), # "split": "test" # }, # ), # datasets.SplitGenerator( # name=datasets.Split.VALIDATION, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": os.path.join(data_dir, "dev.jsonl"), # "split": "dev", # }, # ), # ] def _generate_examples( self, filepath, split, data_dir, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """Yields examples as (key, example) tuples.""" # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. clips_df = pd.read_excel(filepath) with open(filepath, encoding="utf-8") as f: for id_, row in clips_df.iterrows(): data = row bucket = row["bucket"] if bucket == split: yield id_, { "sentence": data["sentence"], "path": data["path"], "audio": os.path.join( data_dir, data["path"] ), # set the audio feature, should be able to handle things automatically? }